code: def test_warning_on_dask_array_without_array_function():
      X, y = make_classification(n_samples=10, n_features=2, chunks=10)
      clf = ParallelPostFit(GradientBoostingClassifier())
      clf = clf.fit(X, y)
  
      class FakeArray:
          def __init__(self, value):
              self.value = value
  
          @property
          def ndim(self):
              return self.value.ndim
  
          @property
          def len(self):
              return self.value.len
  
          @property
          def dtype(self):
              return self.value.dtype
  
          @property
          def shape(self):
              return self.value.shape
  
      ar = FakeArray(np.zeros(shape=(2, 2)))
      fake_dask_ar = da.from_array(ar)
      fake_dask_ar._meta = FakeArray(np.zeros(shape=(0, 0)))
  
      with pytest.warns(
          UserWarning,
          match="provide explicit `predict_meta` to the dask_ml.wrapper",
      ):
          clf.predict(fake_dask_ar)
  
      with pytest.warns(
          UserWarning,
          match="provide explicit `predict_proba_meta` to the dask_ml.wrapper",
      ):
          clf.predict_proba(fake_dask_ar)
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier
  from sklearn.naive_bayes import CategoricalNB
  from sklearn.preprocessing import OneHotEncoder
  from dask_ml.datasets import make_classification
  from dask_ml.utils import assert_eq_ar, assert_estimator_equal
  from dask_ml.wrappers import ParallelPostFit
label_desc: Use image node_id 13 for calling a global function with example usage: test_warning_on_dask_array_without_array_function() and returns: self, self, self, self